IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v309y2024ics0360544224028457.html
   My bibliography  Save this article

Balancing Cost and Economic Efficiency: Consider the multi-purpose optimization of green energy market's function in Green energy interactive infrastructure

Author

Listed:
  • Zhang, Ce
  • Dong, Huijie
  • Xu, Yuanlu
  • Nick, Lucky

Abstract

The increasing reliance on electric micro-mobility vehicles (EMVs) in the delivery industry necessitates efficient battery-swapping infrastructure. This study presents a framework that predicts battery-swapping needs for EMVs using a simulation model that considers the entire travel chain of activities. We propose a multi-objective optimization model to strategically determine battery-swapping station locations, balancing construction and transit costs. Utilizing the Non-dominated Sorting Genetic Algorithm II (NSGA-II), we identified 35 non-dominated solutions within the Pareto front. For Nanjing City, our model indicated that the construction of an optimal network could be achieved with costs ranging from 2.85 to 4.94 million Yuan, corresponding to battery-swapping trip costs between 9700 and 197,000 Yuan. The simulation predicted a daily battery-swapping demand of 675 instances, with peak hours at 11:00 a.m. (301 swaps) and 5:00 p.m. (198 swaps). Sensitivity analysis showed that reducing the charging period from 3 h to 1 h could decrease construction costs by 1.55 million Yuan on average, while maintaining a consistent battery-swapping travel cost of around 23,000 Yuan. Additionally, a 5 % increase in battery-swapping penetration rate led to an average increase of 480,000 Yuan in construction costs and 847 Yuan in travel costs. This study integrates demand forecasting and infrastructure optimization, providing actionable insights for planning and managing battery-swapping stations.

Suggested Citation

  • Zhang, Ce & Dong, Huijie & Xu, Yuanlu & Nick, Lucky, 2024. "Balancing Cost and Economic Efficiency: Consider the multi-purpose optimization of green energy market's function in Green energy interactive infrastructure," Energy, Elsevier, vol. 309(C).
  • Handle: RePEc:eee:energy:v:309:y:2024:i:c:s0360544224028457
    DOI: 10.1016/j.energy.2024.133070
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544224028457
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2024.133070?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Shi, Yishao & Tao, Tianhui & Cao, Xiangyang & Pei, Xiaowen, 2021. "The association between spatial attributes and neighborhood characteristics based on Meituan take-out data: Evidence from shanghai business circles," Journal of Retailing and Consumer Services, Elsevier, vol. 58(C).
    2. R.P. Manatkar & Kondapaneni Karthik & Sri Krishna Kumar & Manoj Kumar Tiwari, 2016. "An integrated inventory optimization model for facility location-allocation problem," International Journal of Production Research, Taylor & Francis Journals, vol. 54(12), pages 3640-3658, June.
    3. Lin, Haiyang & Fu, Kun & Wang, Yu & Sun, Qie & Li, Hailong & Hu, Yukun & Sun, Bo & Wennersten, Ronald, 2019. "Characteristics of electric vehicle charging demand at multiple types of location - Application of an agent-based trip chain model," Energy, Elsevier, vol. 188(C).
    4. Metais, M.O. & Jouini, O. & Perez, Y. & Berrada, J. & Suomalainen, E., 2022. "Too much or not enough? Planning electric vehicle charging infrastructure: A review of modeling options," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    5. Qiang Xing & Zhong Chen & Ziqi Zhang & Xiao Xu & Tian Zhang & Xueliang Huang & Haiwei Wang, 2020. "Urban Electric Vehicle Fast-Charging Demand Forecasting Model Based on Data-Driven Approach and Human Decision-Making Behavior," Energies, MDPI, vol. 13(6), pages 1-32, March.
    6. Liu, Yuechen Sophia & Tayarani, Mohammad & Gao, H. Oliver, 2022. "An activity-based travel and charging behavior model for simulating battery electric vehicle charging demand," Energy, Elsevier, vol. 258(C).
    7. Noor-E-Alam, Md. & Mah, Andrew & Doucette, John, 2012. "Integer linear programming models for grid-based light post location problem," European Journal of Operational Research, Elsevier, vol. 222(1), pages 17-30.
    8. Guo, Fang & Zhang, Jingjing & Huang, Zhihong & Huang, Weilai, 2022. "Simultaneous charging station location-routing problem for electric vehicles: Effect of nonlinear partial charging and battery degradation," Energy, Elsevier, vol. 250(C).
    9. Bo Zhang & Meng Zhao & Xiangpei Hu, 2023. "Location planning of electric vehicle charging station with users’ preferences and waiting time: multi-objective bi-level programming model and HNSGA-II algorithm," International Journal of Production Research, Taylor & Francis Journals, vol. 61(5), pages 1394-1423, March.
    10. Zhang, Fan & Lv, Huitao & Xing, Qiang & Ji, Yanjie, 2024. "Deployment of battery-swapping stations: Integrating travel chain simulation and multi-objective optimization for delivery electric micromobility vehicles," Energy, Elsevier, vol. 290(C).
    11. Michael Kuby & Seow Lim, 2007. "Location of Alternative-Fuel Stations Using the Flow-Refueling Location Model and Dispersion of Candidate Sites on Arcs," Networks and Spatial Economics, Springer, vol. 7(2), pages 129-152, June.
    12. Hosseini, Meysam & MirHassani, S.A., 2015. "Refueling-station location problem under uncertainty," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 84(C), pages 101-116.
    13. Guo, Fang & Yang, Jun & Lu, Jianyi, 2018. "The battery charging station location problem: Impact of users’ range anxiety and distance convenience," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 114(C), pages 1-18.
    14. Xu, Min & Meng, Qiang, 2020. "Optimal deployment of charging stations considering path deviation and nonlinear elastic demand," Transportation Research Part B: Methodological, Elsevier, vol. 135(C), pages 120-142.
    15. Liu, Shan & Jiang, Hai & Chen, Shuiping & Ye, Jing & He, Renqing & Sun, Zhizhao, 2020. "Integrating Dijkstra’s algorithm into deep inverse reinforcement learning for food delivery route planning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 142(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhang, Fan & Lv, Huitao & Xing, Qiang & Ji, Yanjie, 2024. "Deployment of battery-swapping stations: Integrating travel chain simulation and multi-objective optimization for delivery electric micromobility vehicles," Energy, Elsevier, vol. 290(C).
    2. Tran, Cong Quoc & Keyvan-Ekbatani, Mehdi & Ngoduy, Dong & Watling, David, 2021. "Stochasticity and environmental cost inclusion for electric vehicles fast-charging facility deployment," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 154(C).
    3. Ouyang, Xu & Xu, Min, 2022. "Promoting green transportation under the belt and Road Initiative: Locating charging stations considering electric vehicle users’ travel behavior," Transport Policy, Elsevier, vol. 116(C), pages 58-80.
    4. Liu, Yuechen Sophia & Tayarani, Mohammad & You, Fengqi & Gao, H. Oliver, 2024. "Bayesian optimization for battery electric vehicle charging station placement by agent-based demand simulation," Applied Energy, Elsevier, vol. 375(C).
    5. Lin, Haiyang & Bian, Caiyun & Wang, Yu & Li, Hailong & Sun, Qie & Wallin, Fredrik, 2022. "Optimal planning of intra-city public charging stations," Energy, Elsevier, vol. 238(PC).
    6. Böhle, Alexander, 2021. "Multi-Period Optimization of the Refuelling Infrastructure for Alternative Fuel Vehicles," Junior Management Science (JUMS), Junior Management Science e. V., vol. 6(4), pages 790-825.
    7. Chen, Bingkun & Chen, Zhuo & Liu, Xiaoyue Cathy & Zheng, Nan & Xiao, Qijie, 2024. "Measuring the effectiveness of incorporating mobile charging services into urban electric vehicle charging network: An agent-based approach," Renewable Energy, Elsevier, vol. 234(C).
    8. Mahmutoğulları, Özlem & Yaman, Hande, 2023. "Robust alternative fuel refueling station location problem with routing under decision-dependent flow uncertainty," European Journal of Operational Research, Elsevier, vol. 306(1), pages 173-188.
    9. Pourvaziri, H. & Sarhadi, H. & Azad, N. & Afshari, H. & Taghavi, M., 2024. "Planning of electric vehicle charging stations: An integrated deep learning and queueing theory approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 186(C).
    10. Ventura, Jose A. & Kweon, Sang Jin & Hwang, Seong Wook & Tormay, Matthew & Li, Chenxi, 2017. "Energy policy considerations in the design of an alternative-fuel refueling infrastructure to reduce GHG emissions on a transportation network," Energy Policy, Elsevier, vol. 111(C), pages 427-439.
    11. Li, Xiaohui & Wang, Zhenpo & Zhang, Lei & Sun, Fengchun & Cui, Dingsong & Hecht, Christopher & Figgener, Jan & Sauer, Dirk Uwe, 2023. "Electric vehicle behavior modeling and applications in vehicle-grid integration: An overview," Energy, Elsevier, vol. 268(C).
    12. Kınay, Ömer Burak & Gzara, Fatma & Alumur, Sibel A., 2021. "Full cover charging station location problem with routing," Transportation Research Part B: Methodological, Elsevier, vol. 144(C), pages 1-22.
    13. Zhang, Lei & Huang, Zhijia & Wang, Zhenpo & Li, Xiaohui & Sun, Fengchun, 2024. "An urban charging load forecasting model based on trip chain model for private passenger electric vehicles: A case study in Beijing," Energy, Elsevier, vol. 299(C).
    14. Huasheng Liu & Yu Li & Chongyu Zhang & Jin Li & Xiaowen Li & Yuqi Zhao, 2022. "Electric Vehicle Charging Station Location Model considering Charging Choice Behavior and Range Anxiety," Sustainability, MDPI, vol. 14(7), pages 1-19, April.
    15. Mubarak, Mamdouh & Üster, Halit & Abdelghany, Khaled & Khodayar, Mohammad, 2021. "Strategic network design and analysis for in-motion wireless charging of electric vehicles," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
    16. Manzolli, Jônatas Augusto & Trovão, João Pedro & Antunes, Carlos Henggeler, 2022. "A review of electric bus vehicles research topics – Methods and trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    17. Park, Junseok & Moon, Ilkyeong, 2023. "A facility location problem in a mixed duopoly on networks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 175(C).
    18. Arslan, Okan & Karaşan, Oya Ekin, 2016. "A Benders decomposition approach for the charging station location problem with plug-in hybrid electric vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 670-695.
    19. Chen, Jinyu & Zhang, Qiong & Xu, Ning & Li, Wenjing & Yao, Yuhao & Li, Peiran & Yu, Qing & Wen, Chuang & Song, Xuan & Shibasaki, Ryosuke & Zhang, Haoran, 2022. "Roadmap to hydrogen society of Tokyo: Locating priority of hydrogen facilities based on multiple big data fusion," Applied Energy, Elsevier, vol. 313(C).
    20. Liu, Shan & Zhang, Ya & Wang, Zhengli & Gu, Shiyi, 2023. "AdaBoost-Bagging deep inverse reinforcement learning for autonomous taxi cruising route and speed planning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 177(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:309:y:2024:i:c:s0360544224028457. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.